Use CenterStat Curricular Pathways to Guide your Knowledge and Skills Development
CenterStat offers workshops on a variety of topics that can be taken in any order. For those seeking to develop broad expertise within one or more key areas of research methods and statistics, these classes can also be combined into four curricular pathways:
- Foundations Pathway
- Measurement / Latent Variable Modeling Pathway
- Longitudinal Data Analysis Pathway
- Data Science Pathway
Links to upcoming livestream versions of the classes within each pathway are provided below; however, self-paced versions of many of these same classes are also available for immediate access.
Foundations Pathway
Broaden your knowledge with these foundational classes:
- Sample Size Planning for Power and Accuracy: Optimize the effectiveness and efficiency of your research
- Introduction to Data Visualization in R: Wrangle data into stunning statistical graphics with R
- Modern Missing Data Analysis: Learn how to handle missing data due to non-response, attrition, or by design
- FREE Introduction to Structural Equation Modeling: Extend from path analysis and confirmatory factor analysis to SEMs with latent variables
- Multilevel Modeling: Analyze hierarchically or longitudinally nested data (e.g., students within schools)
- Applied Qualitative Research: Address how and why questions by implementing qualitative research designs and methods
Measurement / Latent Variable Modeling Pathway
Improve your ability to conceptualize and measure latent constructs with these classes:
- Applied Measurement Modeling: Use exploratory and/or confirmatory factor analysis to develop valid measures your constructs
- FREE Introduction to Structural Equation Modeling: Extend from path analysis and confirmatory factor analysis to SEMs with latent variables
- Longitudinal Structural Equation Modeling: Determine whether construct measurement shifts over time by testing longitudinal factorial invariance and use latent factors to model change over time
- Mixture Modeling & Latent Class Analysis: Learn about discrete latent variable models, such as latent class/profile analysis
- Network Analysis: Conduct network psychometrics, an emerging alternative to factor analytic approaches to measurement
Longitudinal Data Analysis Pathway
Grow your expertise in longitudinal data analysis with these classes:
- Multilevel Modeling: Learn how to fit a variety of growth curve models within a multilevel modeling framework
- Analyzing Intensive Longitudinal Data: Examine within-person and within-dyad processes in intensive longitudinal data
- Longitudinal Structural Equation Modeling: Get extensive coverage of latent curve models and other SEMs for longitudinal data
- Mixture Modeling & Latent Class Analysis: Use growth mixture models to reveal heterogeneity in change over time
- Modern Missing Data Analysis: Understand how attrition can affect your results, and how to account for it using state-of-the-art methods
Data Science Pathway
Learn how to visualize and mine data with these classes:
- Introduction to Data Visualization in R: Translate theory on graphic design and perception into publication-ready statistical graphics in R
- Network Analysis: Analyze the connections between your observations, whether individuals in a social network, regions of interest in fMRI data, or items in a psychometric network
- Machine Learning: Theory and Applications: Learn about traditional and state-of-the-art approaches to statistical learning (artificial intelligence)
- Mixture Modeling & Latent Class Analysis: Apply unsupervised learning techniques to identify unobserved subgroups and reveal structure in your data